IoT-Based Plant Identification Using Multi-Level Classification
Bibliographic record
Abstract
Accurate plant identification is critical for applications such as automated agriculture and plant monitoring systems. However, traditional classification methods often face challenges in balancing accuracy and computational efficiency, particularly when handling large datasets or real-time processing. This research aims to develop a classification scheme that efficiently identifies plant types based on color and shape attributes, achieving high accuracy with minimal computational complexity. To address this, we propose a two-level classification approach using a Naive Bayes classifier in a hierarchical structure. The first stage utilizes simple color features to categorize the majority of images with high accuracy and low computational overhead. In cases where classification remains uncertain, the second stage extracts additional color and shape attributes, offering a more refined analysis of complex samples. The scheme is implemented within an Internet of Things (IoT)-enabled data acquisition framework, enabling real-time image data collection. The system was evaluated using four types of artificial plants placed in a growth chamber equipped with image sensors and LED lighting, with data processed through a cloud service. The results demonstrate that the two-level classifier outperforms single-level approaches, maintaining high accuracy by deferring more complex samples to the second stage without significantly increasing computational costs. This hierarchical classification scheme successfully balances efficiency and accuracy, making it well-suited for large-scale applications such as smart greenhouses, where reliable and rapid plant classification is essential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".